One example of a learning apparatus is disclosed in Patent literature 1. As shown in FIG. 14, this learning apparatus includes a storage device 1000, a feature point detection unit 1001, a feature amount calculation unit 1002, a vote learning unit 1003, and a classifier learning unit 1004. This learning apparatus has such a feature that, since the learning apparatus identifies an object by voting of feature points, it is relatively robust regarding the difference in shape or the like of each recognition target.
The learning apparatus having such a configuration operates as follows.
The storage device 1000 stores learning images consisting of an image group related to recognition target object and an image group of objects other than the recognition target object. The feature point detection unit 1001 detects a number of feature points from the respective learning images. The feature amount calculation unit 1002 calculates a feature vector as a feature amount related to the feature points and a peripheral image area. The vote learning unit 1003 calculates and stores voting positional information in a parameter space as a voting space for the feature point corresponding to the feature vector calculated from the image related to the recognition target object of the learning image. The classifier learning unit 1004 learns the classifier configured to discriminate whether a given feature point detected in recognition of the recognition target object belongs to the recognition target object using the feature vector.
Patent literature 2 discloses a learning apparatus aimed at improving an identifying performance. The learning apparatus calculates, for each point on a sample image, local information required to recognize a pattern using a rectangular window set around the point. Further, the learning apparatus calculates, for each point on the sample image, arrangement information that specifies identifying classes of areas in the periphery of the marked point. Then the learning apparatus selects one combined information from a plurality of combined information being generated by combining the local information and the arrangement information, to calculate an identifying parameter for one weak classifier based on the combined information that is selected.
Non-patent literatures 1-4 also disclose techniques related to image recognition.